Fair regression methods have the potential to mitigate societal bias concerns in health care, but there has been little work on penalized fair regression when multiple groups experience such bias. We propose a general regression framework that addresses this gap with unfairness penalties for multiple groups. Our approach is demonstrated for binary outcomes with true positive rate disparity penalties. It can be efficiently implemented through reduction to a cost-sensitive classification problem. We additionally introduce novel score functions for automatically selecting penalty weights. Our penalized fair regression methods are empirically studied in simulations, where they achieve a fairness-accuracy frontier beyond that of existing comparison methods. Finally, we apply these methods to a national multi-site primary care study of chronic kidney disease to develop a fair classifier for end-stage renal disease. There we find substantial improvements in fairness for multiple race and ethnicity groups who experience societal bias in the health care system without any appreciable loss in overall fit.

翻译：公平回归方法在医疗保健领域具有缓解社会偏见的潜力，但针对多群体遭受此类偏见时的惩罚性公平回归研究尚少。本文提出一个通用回归框架，通过引入多群体不公平性惩罚项来填补这一空白。我们的方法以二分类结果为例，采用真阳性率差异惩罚进行演示。该方法可通过转化为代价敏感分类问题实现高效计算。此外，我们引入了新颖的评分函数来自动选择惩罚权重。通过仿真实验对惩罚性公平回归方法进行实证研究，结果表明其在公平性-准确性边界上超越了现有对比方法。最后，我们将这些方法应用于全国多中心慢性肾脏病初级护理研究，开发了针对终末期肾病的公平分类器。研究发现，在医疗系统中遭受社会偏见的多个种族和族裔群体，其公平性得到显著改善，且整体拟合度未出现明显损失。